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Miscellaneous. Numerical optimization. Miscellaneous. Dirty Tricks. In this part, after quickly listing the main characteristics. We then jump to more advanced subjects (most of which can - -. The R language. Control structures. Actually, R is a programming language: as such, we have the. Conditionnal statements. Conditionnals may be used inside other constructions. You can also construct vectors from conditionnal. Switch (I do not like this command - - this is probably the. Bonjour'. b='Gutten Tag'. Hello'. d='Konnichi wa'. For loop (we loop over the elements of a vector or list). R belongs to the family of functionnal languages (Lisp. OCa. ML, but also Python): the notion of function is. In particular, if you need it, you can write. A function is defined as follows. The return value is the last value computed - - but you can. Arguments can have default values. When you call a function you can use the argument names. After the arguments, in the definition of a function, you. But you can also use this to write functions that take an. Concatenate all the arguments to form a string. Connect(d. Driver("SQLite")). Get. Query(con, query). Disconnect(con). f < - function (..) {. Put the arguments in a (named) list. Argument name: ", names(l)[i], "Value: ", l[[i]], "\n"). Functions have NO SIDE EFFECTS: all the modifications are. In particular, you cannot write a function that. Well, if you really want, you. Dirty Tricks" part - - but you should not). How to get the code of a function? To get the code of a function, you can just type its name - - wit no. FALSE). diff(quantile(as. FALSE)). < environment: namespace: stats>. But sometimes, it does not work that well: if we want to. Use. Method("predict"). This is a generic function: we can use the same function on. Poisson. or logistic regression, lme for mixed models, etc.). The. actual function called is "predict. Foo" where "Foo" is the. Arima*. [3] predict. Holt. Winters* predict. Struct. TS*. Non- visible functions are asterisked. As we wanted the one for the "lm" object, we just type (I do. FALSE, scale = NULL, df = Inf. NULL, na. action = na. X < - model. matrix(object). Done < - TRUE. But if we wanted the "predict. Error: Object "predict. The problem is that the function is in a given namespace (R. We can get it with the. Anywhere" function (here again, I do not include all the. Anywhere("predict. A single object matching "predict. It was found in the following places. S3 method for predict from namespace stats. Alternatively, we can use the get. S3. Method function. S3method("predict", "prcomp"). Alternatively, if we know in which package a function (or. Error: 'predict. prcomp' is not an exported object from 'namespace: stats'. TRUE, x = FALSE, y = FALSE, qr = TRUE, singular. TRUE. contrasts = NULL, offset, ..). Things can get even more complicated. The most common reason. Actually, quite often, this information is not. Use. Method("print"). As the object belong to the "lm" class. Option("digits") - 3), ..). Call: \n", deparse(x$call), "\n\n", sep = ""). Coefficients: \n"). FALSE). else cat("No coefficients\n"). Same for the "summary" function: it takes the result of a. Use. Method("summary"). FALSE, symbolic. cor = FALSE, ..). Error: Object "print. Anywhere("print. summary. A single object matching "print. It was found in the following places. S3 method for print from namespace stats. Option("digits") - 3), symbolic. Option("show. signif. Call: \n"). cat(paste(deparse(x$cal. But it does not always work.. There are two object- oriented. R: what we have explained works for. Here is an. example for the other. Error: Object "print. Anywhere("print. lmer"). The function is no longer called "print" but "show".. Method("show", "lmer"). Method Definition. Scale = TRUE, show. Correlation = FALSE)). In this case, it simply calls the "summary" function (with. Method("summary", "lmer"). Method Definition. Scale = TRUE, show. Correlation = TRUE). Method("show", "summary. Method Definition. Scale < - object@use. Scale. invisible(object). Functions with side effects. Plotting functions are used for their side effect (the plot that appears. That value can be the result of the computations that lead to the plot. Usually, you do not want the result to be printed, because most users will. To. this end, you can return the value as invisible(): it will not be printed. N=1. 0, FUN=median, ..) {. N)), include. lowest=TRUE). FUN, na. rm=TRUE). Now its gets printed. Some plotting functions return a "plotting object", that can be stored. Does not plot anything. Plots the data. r # Plots the data: print() is implicitely called. An object of class "treillis", so that print(r). Modify the plot. r # Replot it. Operators. The following operators mean what you thing they mean - - but they tend. The boolean operators are !, & et | (but you can write & & or ||. The : (colon) operator creates vectors. The [ operator retrieves one or several elements of a vector, matrix. The $ operator retrieves an element in a list, with no need to put its. The interest of the [[ operator is that is argument can be a. Assignment is written "< -". Some people use "=" instead: this will work most of the time, but not. The matrix product is %*%, tensor product (aka Kronecker product) is %x%. A < - matrix(c(1,2,3,4), nr=2, nc=2). J < - matrix(c(1,0,2,1), nr=2, nc=2). The %o% operator builds multiplication tables (it calls the "outer". A < - 1: 5. > B < - 1. A) < - A. > names(B) < - B. A,B, '*'). 1. 1 1. Euclidian division is written %/%, its remainder %%. Set" membership is written %in%. The ~ and | operators are used to describe statistical model: more about them. For more details (and for the operators I have not mentionned, such as. TODO: mention < < - (and the reverse - > , - > > ). You can also define your own operators: these are just 2- arguments. The following example. Other example, to turn a 2- argument function into an operator, that. Only two arguments. Global variables. TODO: See below ("dirty tricks") for actual global variables - - avoid them. TODO: options(), par()Object Oriented Programming. This is a tricky bit. Object Orientation was added to R as an. The first flavour, S3 classes, is rather simple: you add a "class". C++ programmers would say "virtual" function), e. Foo", the plot. Foo(). The second flavour, S4 classes, is more intricate: it tries to copy. For. large projects, it might be a good idea, but think carefully! More recently, several packages suggested other ways of programming. R: R. oo and proto. Data structures. As all Matlab- like software (remember that "Matlab" stands. Matrix Laboratory" - - it has noting to do with. Mathematics), R handles tables of numbers. Yet, there are. different kinds of tables: vectors (tables of dimension 1). Data Frames" (tables of dimension 2, in which. We shall now. present in more detail each of these, explain how to build. Here are several ways to define them (here, "c" stands for. Here are several ways to select a part of a vector. We remove the elements whose index lies between 5 and 1. FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE. TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE. We can name the coordinates of a vector - - and then access. There is also LETTERS for upper. One can also define those names while creating the vector. A few operations on vectors. T). [1] 1 4 5 3 1 3 4 5 3 1. We need not sort the data before (this contrasts. Unix's "uniq" command). Here are still other ways of creating vectors. The "seq". command generates arithmetic sequences. The "rep" command repeats a number or a vector. It can also repeat each element several times. We can mix the two previous operations. The "gl" command serves a comparable purpose, mainly to. A factor is a vector coding for a qualitatitative variable. We can create them with the "factor" command. Yes", "No", "Perhaps"), 5, replace=T) ). Perhaps Perhaps Perhaps Perhaps No. Levels: No Perhaps. We can specify the list of acceptable values, or "levels" of. Yes", "No", "Perhaps"). T), levels=l ). [1] No Perhaps No Yes Yes. Levels: Yes No Perhaps. Yes" "No" "Perhaps". One can summarize a factor with a contingency table. Yes No Perhaps.
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